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Python Inference

Please check out the FastDeploy is already installed in your environment. You can refer to FastDeploy Installation to install the pre-compiled FastDeploy, or customize your installation.

This document shows an inference sample on the CPU using the PaddleClas classification model MobileNetV2 as an example.

1. Obtaining the model

import fastdeploy as fd

model_url = "https://bj.bcebos.com/fastdeploy/models/mobilenetv2.tgz"
fd.download_and_decompress(model_url, path=".")

2. Backend Configuration

For more examples, you can refer to examples/runtime.

option = fd.RuntimeOption()

option.set_model_path("mobilenetv2/inference.pdmodel",
                      "mobilenetv2/inference.pdiparams")

# **** CPU Configuration ****
option.use_cpu()
option.use_ort_backend()
option.set_cpu_thread_num(12)

# Initialise runtime
runtime = fd.Runtime(option)

# Get model input name
input_name = runtime.get_input_info(0).name

# Constructing random data for inference
results = runtime.infer({
    input_name: np.random.rand(1, 3, 224, 224).astype("float32")
})

print(results[0].shape)

When loading is complete, you will get the following output information indicating the initialized backend and the hardware devices.

[INFO] fastdeploy/fastdeploy_runtime.cc(283)::Init	Runtime initialized with Backend::OrtBackend in device Device::CPU.

PaddlePaddle / FastDeploy

Python Inference

1. Obtaining the model

2. Backend Configuration

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Python Inference

1. Obtaining the model

2. Backend Configuration

Other Documents

简介

发行版

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